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Today's densely instrumented world offers tremendous opportunities forcontinuous acquisition and analysis of multimodal sensor data providingtemporal characterization of an individual's behaviors. Is it possible toefficiently couple such rich sensor data with predictive modeling techniques toprovide contextual, and insightful assessments of individual performance andwellbeing? Prediction of different aspects of human behavior from these noisy,incomplete, and heterogeneous bio-behavioral temporal data is a challengingproblem, beyond unsupervised discovery of latent structures. We propose aSupervised Tensor Embedding (STE) algorithm for high dimension multimodal datawith join decomposition of input and target variable. Furthermore, we show thatfeatures selection will help to reduce the contamination in the prediction andincrease the performance. The efficiently of the methods was tested via twodifferent real world datasets.

Tensor Embedding: A Supervised Framework for Human Behavioral Data Mining and Prediction